The quality of the reconstruction closely relates to the extracted information from the pre-trained model. When more information is exploited from the pre-trained model, data-free compression achieves better performance. Thus, the current reconstruction methods [1]}, [2]}, [3]}, [4]}, [5]}, [6]} focus on exploiting as much prior information as possible from the pre-trained model. However, how the network engineering will contribute to the reconstruction method remains unknown. Thus, we consider network engineering of the reconstruction method for the first time in the literature. This work aims to seek an optimized generator architecture, with which data-free compression shows performance improvement. It is worth mentioning that network engineering of the reconstruction and exploiting more prior information from the pre-trained model are complementary rather than contradictory. Both are important and should be explored for improving data-free compression. The contribution of this paper is summarized as follows.
